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All Journal Hanif Journal of Information Systems Journal of Progressive Information, Security, Computer and Embedded System Al'Adzkiya International of Computer Science and Information Technology Journal
Akhmad Jayadi
Politeknik Negeri Lampung
Computer Science & IT Engineering Library & Information Science

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Improving the Accuracy of Lettuce and Weed Classification Based on MobileNetV2 Features Through Segmentation Akhmad Jayadi; Kurniawan Saputra; Ahmad Rofi'i
Hanif Journal of Information Systems Vol. 3 No. 2 (2026): February Edition
Publisher : Ilmu Bersama Center

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Abstract

Automating the separation of commodity crops and weeds is a major challenge in the implementation of precision agriculture . The presence of complex backgrounds such as soil, rocks, and shadows often degrades the performance of feature extraction in computer vision classification models. This study proposes an image preprocessing approach using the GrabCut segmentation method to extract key crop objects cleanly before performing Deep Learning- based feature extraction . Representative features from the image are extracted using the lightweight and efficient MobileNetV2 architecture. Next, classification is performed by comparing three Machine Learning algorithms , namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). Testing is carried out on two data scenarios, namely the original dataset ( Original ) and the segmented dataset ( GrabCut ). The experimental results show that the use of original images produces an accuracy of 98.89% for all three classification models. However, after being integrated with GrabCut segmentation, the accuracy of all three models increases significantly to 100.00%. These results prove that GrabCut-based segmentation effectively eliminates background noise information , thereby improving the generalization capabilities of classification models perfectly on edge computing devices .
Comparative Analysis of Radix Sort, Quick Sort, and Bubble Sort Algorithms in Data Sorting Based on Array Size and Time Kurnia Wati Fadhillah; Nandy Thaher Ulga; Raffi Ramadhan Oktaviansyah; Farah Sulistia; Jeni Amanda; Akhmad Jayadi
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 6, No 2 (2025)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v6i2.354

Abstract

An algorithm is a series of logical actions used to solve important problems in contemporary programming and data processing. The purpose of this study is to compare the time efficiency of three sorting algorithms: Bubble Sort, Radix Sort, and Quick Sort. All algorithms are used on small (10-100 elements), medium (1,000-10,000 elements), and large (more than 100,000 elements) arrays, with execution time using Java. The results show that Radix Sort and Quick Sort are generally more efficient and scalable than Bubble Sort, especially for large arrays and random or semi-sorted data. Radix Sort excels on small and medium arrays under various conditions, while Quick Sort excels on large arrays in the average and nearly sorted cases. Although Bubble Sort can be the fastest in the best case for large arrays, its performance drops drastically in the average and nearly sorted cases. In conclusion, the selection of the best sorting algorithm depends heavily on the type of input data, such as its size and the degree of initial sorting.
Computer Vision-Based Lettuce and Plantweed Segmentation Using YOLO and Segment Anything for Precision Agriculture Akhmad Jayadi; Adi Ahmad Fauzi; Muhammad Ikhsan; Jaka Persada Sembiring; Ahmad Rofi'i
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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Abstract

The presence of weeds in lettuce cultivation areas can reduce plant productivity because they compete for nutrients, water, and light. Manual weed identification requires considerable time and effort, so an automated system based on computer vision is needed . This study proposes the integration of the YOLO11s model and the Segment Anything Model (SAM) to detect and segment lettuce plants and weeds in agricultural environments. The dataset used consists of 741 training images , 212 validation images , and 106 testing images with two object classes, namely Lettuce and Plantweed . The YOLO11s model was trained for 100 epochs using an image size of 640 × 640 pixels and a batch size of 106 . 16. The training results show that the model obtained an mAP@50 value of 87.8%. And mAP@50–95 was 80.0% , indicating good object detection capability. Furthermore, the bounding box coordinates of the detection results were used as prompts in the Segment Anything Model to generate mask -shaped segmentations that follow the object contours more precisely. The experimental results show that the integration of YOLO11s and SAM is able to produce more detailed object representations compared to detection using bounding boxes alone. This approach has the potential to support various precision agriculture applications, such as plant morphology analysis, leaf area estimation, and the development of automated weed control systems .
Analisis Kebutuhan Fitur Minimum pada Klasifikasi Lettuce dan Weed Berbasis MobilenetV2 dan Support Vector Machine Akhmad Jayadi; Ahmad Rofi'i; Kurniawan Saputra
Progressive Information, Security, Computer, and Embedded System Vol. 4, No. 1 Maret (2026)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v4i1.1388

Abstract

Automating weed removal in lettuce cultivation requires a lightweight and fast computer vision system for implementation on mobile or edge devices. This study aims to analyze the minimum number of features extracted from the MobileNetV2 architecture to accurately classify lettuce and weeds. Features extracted from the global average pooling layer of MobileNetV2 yielded 1,280 base features. The SelectKBest method with Mutual Information criteria was used to reduce the feature dimensionality, followed by classification using a Support Vector Machine (SVM) based on the Radial Basis Function (RBF) kernel. Experimental results showed that the model achieved 100% accuracy using only two minimum features, representing a feature reduction of 99.84%. This feature reduction significantly speeds up computation time, making it ideal for mobile-based computing in the smart agriculture sector.
Co-Authors Adi Ahmad Fauzi Ahmad Rofi'i Farah Sulistia Jaka Persada Sembiring Jeni Amanda Kurnia Wati Fadhillah Kurniawan Saputra Muhammad Ikhsan Nandy Thaher Ulga Raffi Ramadhan Oktaviansyah